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arXiv:2610.31824

A Topology-Aware Quantum-Classical Framework for Multi-Target Active Debris Removal using Gaussian Boson Sampling

Komal Pandya, Saiyam Sakhuja, Nagaraj Kattaian

cs.CEcs.ETquant-phSubmitted Oct 22, 2026• Announced Oct 26, 2026• 0 citations• Revised Oct 26, 2026 (v2)
v1 · Oct 22, 2026v2 · Oct 26, 2026
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Abstract

The continued growth of orbital debris threatens the safety and sustainability of near-Earth space, making Active Debris Removal (ADR) an important mitigation strategy. Selecting $k$ targets from $N$ candidates yields a combinatorial search space of size $\binom{N}{k}$, making joint target selection and mission sequencing computationally demanding. We present a topology-aware quantum-classical framework integrating orbital mechanics, weighted orbital-transfer graphs, Gaussian Boson Sampling (GBS), and multi-objective evolutionary optimization. Seven target-prioritization strategies represent physical consequence, orbital persistence, conjunction risk, network centrality, community structure, inferred source families, and random selection. GBS generates candidate target subsets from the weighted graph, which are then evaluated using PyGMO-based evolutionary optimization to identify servicing sequences that jointly minimize total velocity increment ($Δv$) and mission duration, accounting for modeled timing, propulsion, and mass feasibility. We evaluate the framework across three benchmark environments, SOCRATES Minefield, Collision Cloud, and Random, with candidate-pool sizes $N\in\{15,18,22,25\}$ and target cardinalities $k\in\{3,5\}$. The experiments characterize how benchmark topology, prioritization strategy, and problem scale relate to sampling behavior and downstream mission performance. By separating candidate-subset generation from detailed trajectory optimization, the framework offers a topology-aware approach to focusing computational effort on a limited set of candidate ADR missions.

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Abstract The rapid accumulation of space debris in Low Earth Orbit (LEO) poses a significant challenge to the sustainability of space operations. While preventive measures limit new debris generation, they are insufficient to mitigate the growing population of defunct satellites, rocket stages, and collision fragments. Active Debris Removal (ADR) has emerged as a viable solution, which requires solving NP-hard combinatorial optimization problemsimilar to the Traveling Salesman Problem (TSP) to maximize mission efficiency by minimizing fuel and mission duration. This work explores the application of Quantum Annealing (QA) and Hybrid Quantum Annealing (HQA) for optimizing multi-target ADR missions. Specifically, it introduces a Quadratic Unconstrained Binary Optimization (QUBO) model for ADR, exploiting quantum computing to enhance solution efficiency. A novel quadratization method is developed to reduce computational complexity, enabling large-scale mission planning. Additionally, a novel constraint-handling strategy is proposed, integrating mission constraints into post-processing to enhance quantum solver efficiency. The proposed approach is validated using real-world satellite debris datasets and benchmarked against classical metaheuristic optimizers, including Simulated Annealing (SA), Tabu Search (TS), and Genetic Algorithms (GA). The obtained results demonstrate the advantages of quantum optimization for ADR mission planning, providing a scalable and computationally efficient solution.
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A strategy for the analysis of active debris removal missions targeting multiple objects from a set of objects in near-circular orbit with similar inclination is presented. Algebraic techniques successfully reduce the orbital mechanics regarding specific inter-debris transfer and disposal methods to simple computations, which can be used as the coefficients of a quadratic unconstrained binary optimisation (QUBO) problem formulation which minimises the total propellant used in the mission whilst allowing for servicing time and meeting the mission deadline. The QUBO is validated by solving artificial small problems (from 2 to 11 debris) using classical computational methods and the weaknesses in using these methods are examined prior to solution using quantum annealing hardware. The quantum processing unit (QPU) and quantum-classical hybrid solvers provided by D-Wave are then used to solve the same small problems, with attention paid to evident strengths and weaknesses of each approach. Hybrid solvers are found to be significantly more effective at solving larger problems. Finally, the hybrid method is used to solve a large problem using a real dataset. From a set of 79 debris objects resulting from the destruction of the Kosmos-1408 satellite, an active debris removal mission starting on 30 September 2023 targeting 5 debris objects for disposal within a year with 20 days servicing time per object is successfully planned. This plan calculates the total propellant cost of transfer and disposal to be 0.87km/s and would be complete well within the deadline at 241 days from the start date. This problem uses 6,478 binary variables in total and is solved using around 25s of QPU access time.
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Gaussian boson sampling (GBS) is not only a feasible protocol for demonstrating quantum computational advantage, but also mathematically associated with certain graph-related and quantum chemistry problems. In particular, it is proposed that the generated samples from the GBS could be harnessed to enhance the classical stochastic algorithms in searching some graph features. Here, we use Jiǔzhāng, a noisy intermediate-scale quantum computer, to solve graph problems. The samples are generated from a 144-mode fully connected photonic processor, with photon click up to 80 in the quantum computational advantage regime. We investigate the open question of whether the GBS enhancement over the classical stochastic algorithms persists-and how it scales-with an increasing system size on noisy quantum devices in the computationally interesting regime. We experimentally observe the presence of GBS enhancement with a large photon-click number and a robustness of the enhancement under certain noise. Our work is a step toward testing real-world problems using the existing noisy intermediate-scale quantum computers and hopes to stimulate the development of more efficient classical and quantum-inspired algorithms.